Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations
This work addresses computational bottlenecks in nano-electronics simulations for researchers and engineers, representing an incremental improvement by applying a novel machine learning method to an existing simulation framework.
The authors tackled the slow convergence of quantum mechanical device simulations by introducing ML-NEGF, a method combining convolutional generative networks with non-equilibrium Greens function simulations, achieving an average 60% acceleration in convergence speed while maintaining accuracy.
This work describes a novel simulation approach that combines machine learning and device modelling simulations. The device simulations are based on the quantum mechanical non-equilibrium Greens function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the standard NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML- NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.